A HIERARCHICAL BAYESIAN ANALYSIS OF HORSE RACING

Horse racing is the most popular sport in Hong Kong. Nowhere else in the world is such attention paid to the races and such large sums of money bet. It is literally a “national sport”. Popular literature has many stories about computerized “betting teams” winning fortunes by using statistical analysis.[1] Additionally, numerous academic papers have been published on the subject, implementing a variety of statistical methods. The academic justification for these papers is that a parimutuel game represents a study in decisions under uncertainty, efficiency of markets, and even investor psychology. A review of the available published literature has failed to find any Bayesian approach to this modeling challenge.This study will attempt to predict the running speed of a horse in a given race. To that effect, the coefficients of a linear model are estimated using the Bayesian method of Markov Chain Monte Carlo. Two methods of computing the sampled posterior are used and their results compared. The Gibbs method assumes that all the coefficients are normally distributed, while the Metropolis method allows for their distribution to have an unknown shape. I will calculate and compare the predictive results of several models using these Bayesian Methods

COMPARING THE EFFECTIVENESS OF ONE- AND TWO-STEP CONDITIONAL LOGIT MODELS FOR PREDICTING OUTCOMES IN A SPECULATIVE MARKET

This paper compares two approaches to predicting outcomes in a speculative market, the horse race betting market. In particular, the nature of one-and two-step conditional logit procedures involving a process for exploding the choices et are outlined, their strengths and weaknesses are compared and the irrelative effectiveness is  evaluated by predicting winning probabilities for horse races at a UK racetrack. The models incorporate variables which are widely recognised as having predictive power and which should therefore be effectively discounted in market odds. Despite this handicap, both approaches produce probability estimates which can be used to earn positive returns, but the two-step approach yields substantially higher profits

ADAPTING LEAST-SQUARE SUPPORT VECTOR REGRESSION MODELS TO FORECAST THE OUTCOME OF HORSERACES

This paper introduces an improved approach for forecasting the outcome of horseraces. Building upon previous literature, a state-of-the-art modelling paradigm is developed which integrates least-square support vector regression and conditional logit procedures to predict horses’ winning probabilities. In order to adapt the least-square support vector regression model to this task, some free parameters have to be determined within a model selection step. Traditionally, this is accomplished by assessing candidate settings in terms of mean-squared error between estimated and actual finishing positions. This paper proposes an augmented approach to organise model selection for horserace forecasting using the concept of ranking borrowed from internet search engine evaluation. In particular, it is shown that the performance of forecasting models can be improved significantly if parameter settings are chosen on the basis of their normalised discounted cumulative gain (i.e. their ability to accurately rank the first few finishers of a race), rather than according to general purpose performance indicators which weight the ability to predict the rank order finish position of all horses equally

Predicción de resultados de eventos deportivos : carreras de caballos

En la actualidad las apuestas deportivas están cobrando una gran importancia a nivel mundial. Cada vez es más común que realicen apuestas las personas menos especializadas, que ven en las mismas una vía de escape tanto como diversión como forma de hacer fortuna. En este Proyecto Fin de Carrera nos centramos en las apuestas de carreras de caballos. Para ello vamos a tener como objetivo pronosticar qué caballo es el que va a resultar ganador en cada carrera. Y de esta forma, obtener beneficios con nuestras apuestas. Construiremos una base de datos con carreras de Sudáfrica desde Febrero de 2011 hasta Junio de 2011, las cuáles han sido extraídas de la página web Formstar. A través de la base de datos, entrenaremos un clasificador por medio del método de Máquinas de Vectores Soporte, con los parámetros adecuados, seleccionados con validación cruzada. De esta forma, cuando tengamos nuevas carreras, nuestro sistema será capaz de decirnos las probabilidades de que cada caballo gane la carrera, y podremos apostar con dicha información disponible.Ingeniería Técnica en Sonido e Image